Need a Mondrian .WAR? Check out XMondrian.

To whom it may concern, this is a quick note to bring the xmondrian project to your attention.

Introduction: Open Source OLAP, Mondrian, Pentaho, and JasperSoft

Mondrian is the open source OLAP engine. Mondrian provides:

• a multi-dimensional view of a relational database (ROLAP)
• a MDX query engine
• Clever, advanced caching layers to speed up OLAP query performance (making it a MOLAP/ROLAN hybrid i.e., HOLAP)
• Standards compliant OLAP data access by providing XML for Analysis (XML/A) and OLAP4J access APIs

Mondrian was designed and invented by Julian Hyde, who acted as technical and architectural lead of the Mondrian project for many years.

Mondrian was adopted by Pentaho, and is included in the Pentaho BI Stack as Pentaho Analysis Services. Mondrian is also the OLAP engine that ships with the Tibco/JasperSoft Reporting server, and with Meteorite BI's Saiku product.

Running Mondrian Standalone

While Pentaho, Jaspersoft and Meteorite all do a good job of integrating Mondrian inside their respective BI servers, some people would like to run only Mondrian directly in their java servers. The Mondrian project used to make that quite easy, since it shipped a .WAR (web-archive) file containing Mondrian itself, documentation, sample cubes, and the JPivot mondrian client.

Unfortunately, the Mondrian project stopped supporting the .WAR and sample content. This happpened a while ago already, but there are still people that are finding out about it only now. This might have to do with the fact that the Mondrian documentation has not been very well maintained and still refers to the .WAR as if it is part of the Mondrian project.

Introducing XMondrian

I felt the need to have a Mondrian .WAR myself. Main reason is that I created a couple of OLAP client tools myself, and I want to provide potential users with a quick and easy path to check them out. So, I decided to pack them all in a .WAR, together with Mondrian, the Foodmart Sample cube, and an embedded dataset.

The result is called xmondrian which you can find on github.

Getting started with XMondrian

Getting started with XMondrian is easy:

• Download the .WAR file
• Deploy to your java server. In theory, the process to do that will be dependent upon which webserver you are running. I tried with Apache Tomcat, Jetty, and Tiny Java Web Server, and for all these products you can simply copy the .WAR to the webapps directory
• Find the XMondrian homepage by navigating your browser to the xmondrian webapp. For example, suppose you installed Tomcat or Jetty locally, using the default port of 8080, then http://localhost:8080/xmondrian will bring you there.

What's inside XMondrian

Once you're on the XMondrian homepage, you can find more information about what's inside, but I'll summarize below:

• Mondrian 3.12
• A web.xml to instantiate and hook up the MondrianXmlaServlet. After installation of xmondrian, your webserver can receive XML/A requests via /xmondrian/xmla
• HSQLDB embedded database engine
• Sample Datasets and Schemas Both the Foodmart and Steelwheels datasets are included as embedded hsqldb database in a .jar file. There are predefined Mondrian Schema files for each dataset as well, which specify how these databases are mapped to cubes, measures, dimensions, etc. Finally, there are datasource files that tell mondrian to connect to the sample database and use the respective schema file
• xmla4js - A javascript XML/A client library. You can use this in browser-based web applications to communicate with Mondrian via the XML/A protocol. Xmla4js ships with code samples as well as API documentation
• Client Applications
  • XMLash - XML/A Shell: an interactive MDX command line interface for inspecting Mondrian schema objects, an for creating and running MDX queries. (See a demonstration )
  • Xavier - XML/A Visualizer: an interactive OLAP ad-hoc reporting and charting tool with a graphical user interface

The XML/A Shell Application

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The XML/A Visualizer Application

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Finally

I hope this post was useful to you. Please let me know how you get along with the xmondrian .WAR. I'm open to suggestions and I would love to collaborate to make xmondrian better. Please use the github issue tracker to provide your feedback. Thanks for your time and interest.

NoSQL support lands in JasperSoft

JasperSoft, one of the leading open source BI suites just announced it is delivering connectors for a range of so-called NoSQL databases. The big names are all there: Cassandra, MongoDB, Riak, HBase, CouchDB, Neo4J, Infinispan, VoltDB and Redis.

I used to explain to people that the lack of SQL support in NoSQL databases poses a challenge for traditional Business Intelligence tools, because those all talk either SQL or MDX (and maybe some XQuey/XPath). With this development, this is no longer true, and I want to congratulate JasperSoft in spearheading this innovation.

I still have a number of reservations though. Although I personally value the ability to report on data in my NoSQL database, I think its usefulness will hava a number of limitations that are worth consideration.

Admittedly I am not an expert in the NoSQL database field, but as far my knowledge goes, both the dynamo-style key/value stores like Riak, and the Bigtable-style hashtable stores like HBase and Cassandra can basically do 2 types of read operations: fetch a single object by key, or scan everything. The fetched object can be complex and contain a lot of data, and it would certainly be nice if you could run a report on that. The scan everything operation doesn't seem that useful at the report level: for all but trivial cases, you need considerable logic to make this scan useful, and I don't think a report is the right place for this. Apart from that, if the NoSQL solution was put in place because of the large data volume, then the report itself would probably need to be executed on a cluster just to achieve acceptable response time. I may be wrong but I don't think JasperReports supports that.

So, for a full scan of those NoSQL databases, connectors at the data integration end seem more appropriate. I think the integration of Hadoop with Pentaho data integration (a.k.a Kettle) is a step in the right direction, but of course only applicable if you're a Hadoop user.

Another point is data quality. Typically reporting is done on a data warehouse or reporting environment where the data quality is kept in check by processing the raw data with a data integration and quality tools. Directly reporting on any operational database can be problematic because you skip those checks. Because the NoSQL databases offer virtually no constraints, those checks are even more important. So to me this seems like another reason why NoSQL connectivity is more useful in the data integration tools.

JasperSoft also offers connectivity for the MongoDB and CouchDB docmentstores. I think that for raw reporting on the actual source documents, the same reservations apply as I mentioned in relation to the dynamo and Bigtable style solutions. But, there may be a few more possibilities here, at least for CouchDB

CouchDB has a feature called views, which allows you to "query" the raw documents using a map/reduce job. I can certainly see why it'd be useful to build a report on top of that. Of course, you would still have to implement the logic to do a useful scan, and you would still have to deal with data quality issues, but you can do it in the map/reduce job, which seems a more appropriate place to handle this than a report.

All in all, I think this is a promising development, and I should probably get my feet wet and try it out myself. But for now, I would recommend to keep it out of the wrecking tentacles of unaware business users :)